# Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis

arXiv: https://arxiv.org/abs/2305.00042 · HTML: https://mlsanity.com/paper/2305.00042/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022)
- [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://mlsanity.com/paper/2210.05559/) (2022)
- [Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT](https://mlsanity.com/paper/2304.04920/) (2023)
- [MRI Cross-Modality NeuroImage-to-NeuroImage Translation](https://mlsanity.com/paper/1801.06940/) (2018)

## Built on by (most cited first, 18 of 18)

- [Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model](https://mlsanity.com/paper/2305.19467/) (2023)
- [cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis](https://mlsanity.com/paper/2411.17203/) (2024)
- [Deep Generative Models for 3D Medical Image Synthesis](https://mlsanity.com/paper/2410.17664/) (2024)
- [MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting](https://mlsanity.com/paper/2503.01576/) (2025)
- [A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI](https://mlsanity.com/paper/2311.11383/) (2023)
- [Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation](https://mlsanity.com/paper/2403.01619/) (2024)
- [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025)
- [McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis](https://mlsanity.com/paper/2409.00585/) (2024)
- [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024)
- [LPUWF-LDM: Enhanced Latent Diffusion Model for Precise Late-phase UWF-FA Generation on Limited Dataset](https://mlsanity.com/paper/2409.00726/) (2024)
- [Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation](https://mlsanity.com/paper/2603.12581/) (2026)
- [Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model](https://mlsanity.com/paper/2403.18139/) (2024)
- [Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI](https://mlsanity.com/paper/2505.03498/) (2025)
- [Mitigating analytical variability in fMRI results with style transfer](https://mlsanity.com/paper/2404.03703/) (2024)
- [Generalizable 7T T1-map Synthesis from 1.5T and 3T T1 MRI with an Efficient Transformer Model](https://mlsanity.com/paper/2507.08655/) (2025)
- [An Efficient 3D Latent Diffusion Model for T1-contrast Enhanced MRI Generation](https://mlsanity.com/paper/2509.24194/) (2025)
- [Any-to-All MRI Synthesis: A Unified Foundation Model for Nasopharyngeal Carcinoma and Its Downstream Applications](https://mlsanity.com/paper/2602.08822/) (2026)
- [MRI super-resolution in ten sampling steps using a diffusion bridge model](https://mlsanity.com/paper/2608.08819/) (2026)

## Abstract (arXiv)

This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each other to generate synthetic images from two different MRI pulse sequences. The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities. This improves image-to-image translation ac-curacy. We evaluated the CG-DDPM quantitatively using mean absolute error (MAE), multi-scale structural similarity index measure (MSSIM), and peak sig-nal-to-noise ratio (PSNR), as well as the network synthesis consistency, on the BraTS2020 dataset. Our proposed method showed high accuracy and reliable consistency for MRI synthesis. In addition, we compared the CG-DDPM with several other state-of-the-art networks and demonstrated statistically significant improvements in the image quality of synthetic MRIs. The proposed method enhances the capability of current multimodal MRI synthesis approaches, which could contribute to more accurate diagnosis and better treatment planning for patients by synthesizing additional MRI modalities.